Satellite-based positioning systems, such as Global Navigation Satellite Systems (GNSS), are commonly used to provide accurate positioning. However, the accuracy of such systems significantly deteriorates where satellite signals are weak or blocked, such as in dense urban areas or indoors. As a result, alternative positioning techniques that can provide strong coverage (e.g. electromagnetic wireless signals) in environments where access to reference-based positioning is degraded or denied have been developed. One such system comprises IEEE 802.11 Wireless Local Area Network (WLAN), and is commonly referred to as “Wi-Fi”.
Wireless positioning depends upon the characteristics and quality of transmitted wireless signals, and sub meter-level accuracy can be obtained where the signal characteristics (e.g. signal power, direction and travel time) are of sufficient quality. For example, “time-based” wireless positioning systems, which depend upon signal time of flight from signal transmitters to receivers, can provide accurate positioning where the time of signal flight portrays an accurate indication of the distance between the transmitters and receivers and if a clear line-of-sight exists. Similarly, “direction-based” wireless positioning systems, which depend upon the “direction of arrival” or “angle of arrival” of the transmitted signal arriving at the receiver, are also used where there is a clear line-of-sight. However, such systems fail where there is no clear line-of-sight, or where the signal is reflected or refracted on different surface types (i.e. a “multipath effect”).
In an attempt to overcome multipath effects, “signal-strength-based” wireless positioning systems, which depend upon the strength of the signal received, have been developed. Such systems generally utilize modeling method to either map the signal strength received from a plurality of transmitters:
a) to a particular distance from the transmitters (e.g. a propagation model), or
b) directly to a location using a pre-collected radio survey database (“radio map”), whereby the radio map references known locations in the area and corresponding received signal strength patterns.
One problem associated with the foregoing “signal-strength-based” systems that utilize a propagation model in indoor areas is the indifference to direction of the received signal arriving at the receiver. Where the area is complex in nature, signal attenuation with distance may not be the same in all directions. Attempts to remedy this problem have been to incorporate additional hardware, such as directional antennas, into the environment. However, the additional hardware requirement can be costly, and is not feasible or practical in all scenarios.
Another problem associated with “strength-based” systems is the dependency upon pre-calibrated or offline-trained models before such systems can be used. Data collection and training offline require additional time and effort by the user. Although it is possible to automate these processes such as, for example, incorporating additional hardware for automated data collection (e.g. using wireless monitors or mobile robots), the additional hardware is costly, and is not feasible or practical in all locations.
Current wireless positioning systems are also plagued with the fact that many environments, including indoor environments, can undergo dynamic and frequent (or continuous) changes. Any trained propagation model or collected radio map can therefore quickly become out-of-date. Again, extra hardware, such as wireless sensors located in different areas within the environment, can be positioned and used to update the models using the most recent data possible. Alternatively, some systems may predict (instead of physically measuring) signal strengths using detailed pre-knowledge about the environment and specialized ray-tracing simulation software to predict the signal strength in any location in the environment. As such, current systems still require extra hardware or pre-knowledge about the environment. These systems cannot be used in new or unknown environments, and even where pre-knowledge about an area is available, current updates would be required in order to maintain accurate models of the area.
Finally, another drawback of existing wireless positioning systems is the inability of such systems to provide precise accuracy measures or expected error of the calculated positions. Furthermore, current systems are plagued with the problem of data irrelevancy (i.e. the use of insignificant information that can exist in WLAN areas), thereby deteriorating the accuracy of the solution provided and unnecessarily increasing processing time. Accurate positioning is further complicated where the exact location of signal transmitters are not known without pre-knowledge of the area.
There is therefore a need for a more accurate, flexible, and reliable wireless positioning system and method for use in environments having WLAN coverage, but degraded, denied or inaccurate access to reference-based positioning systems. Such a system may not depend on previously determined maps of the area, offline radio scans and surveys, extra network hardware, or simulation software, while still providing accurate positioning and accuracy measures that are not impacted by changes in the environment as well as accuracy measures thereof. Further, such a system may continuously or periodically and automatically adapt to changes in the environments without human efforts or time-consuming offline re-training of the signal-strength-models of the environment.